arXiv AI
Jul 14
IG-GAN: A Generative Adversarial Network for Aerodynamic Data Generation Based on Intrinsic Geometry
arXiv:2607. 11497v1 Announce Type: cross Abstract: Existing generative models learn data distributions in flat Euclidean space.
Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling
arXiv:2607. 12143v1 Announce Type: cross Abstract: Accurately modeling the dynamics of planetary parachute and entry vehicle systems is critical for Entry, Descent, and Landing events such as vehicle separation and sensor activation.
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arXiv Machine Learning
Jul 10
Unlocking Temporal Generalization in Hamiltonian Video Dynamics Models
arXiv:2607. 07763v1 Announce Type: new Abstract: World models are typically trained to predict discrete-time physical dynamics with a fixed step size baked into the model weights, preventing prediction at variable temporal resolutions.
arXiv Machine Learning
Jun 11
Least-Action-Guided Diffusion for Physical Extrapolation
arXiv:2606. 11277v1 Announce Type: new Abstract: Reliable extrapolation remains a central challenge for generative models in computational physics, because models trained over finite ranges of time, parameters, or geometries may produce physically inconsistent predictions outside the training distribution.
arXiv AI
Jun 15
SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model
arXiv:2601. 18707v2 Announce Type: replace-cross Abstract: Machine learning-based surrogate models have emerged as more efficient alternatives to numerical solvers for physical simulations over complex geometries, such as car bodies.
arXiv AI
Jul 3
Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
arXiv:2605. 00412v3 Announce Type: replace Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and model-based reinforcement learning.
Hugging Face Trending Papers
Jun 22
Policy-as-Data: Learning Generalizable HOI Diffusion Models from Simulated Physics
Synthesizing realistic Human-Object Interactions (HOI) is critical for creating embodied avatars and functional virtual environments. However, current data-driven approaches primarily rely on motion capture datasets, which are expensive to scale and limited in functional diversity.
arXiv Machine Learning
Jun 15
Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
arXiv:2512. 13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling.
arXiv Machine Learning
Jun 19
Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
arXiv:2604. 21097v2 Announce Type: replace-cross Abstract: Chaos arises in many complex dynamical systems, from weather to power grids, but is difficult to accurately model with data-driven methods such as machine learning emulators.
arXiv AI
Jul 14
Knowledge-Constrained Shape Optimization with a Mixture-of-Experts Neural Operator for High-Confidence Design
arXiv:2607. 09763v1 Announce Type: cross Abstract: Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability.
arXiv Machine Learning
Aug 5
Symplectic Neural Networks for Learning Non-Separable Hamiltonians
arXiv:2606. 27029v2 Announce Type: replace Abstract: Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency.
arXiv Machine Learning
Jun 4
Learning symplectic model reduction based on a approximation theorem of symplectic embeddings
arXiv:2606. 04623v1 Announce Type: new Abstract: High-dimensional Hamiltonian systems play a central role in many scientific and engineering disciplines, with dynamics evolving on symplectic manifolds.
arXiv Machine Learning
Jun 26
Symplectic Neural Networks for learning Generalized Hamiltonians
arXiv:2606. 27029v1 Announce Type: new Abstract: Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency.